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Emerging Research in Intelligent New Energy Vehicles
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Emerging Research in Intelligent New Energy Vehicles

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (26 May 2023) | Viewed by 13051

Special Issue Editors

Dr. Chao Sun

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China
Interests: prediction, control and decision-making of new energy vehicles
Dr. Dong Zhang

E-Mail Website
Guest Editor
Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA
Interests: energy storage systems; dynamic systems and control; electrified transportation
Dr. Xingyu Zhou

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China
Interests: electric vehicles; powertrain optimization; energy management and eco-driving

Special Issue Information

 Dear Colleagues,

Intelligent new energy vehicles provide revolutionary means for next-generation transportation to keep the environment clean, sustainable, and resilient from pollutant and carbon emissions. These vehicles are mainly propelled by eco-friendly power sources and will become an integral part of future smart cities through wireless communication networks that connects vehicles, travelers, infrastructure, and services.

Frontiers of Intelligent New Energy Vehicles is a special issue of Sustainability and provides a forum for the latest scientific and technological advances in vehicular electrification and intelligentization for promoting the formulation of next-generation smarter and cleaner transportation systems, especially at present energy saving and carbon reduction are gaining more attention than ever. This special issue focuses on onboard energy storage technologies/devices, advanced propulsion systems for new energy vehicles (NEVs), analysis and optimization of energy conversion processes in new energy vehicles, and connected/automated vehicle technologies. Fields of interest include:

  • Real-time battery management systems in NEVs
  • Advanced propulsion devices/systems of NEVs
  • Pure electric powertrain systems
  • (Plug-in) hybrid electric powertrain systems
  • Fuel cell powertrain systems, and hydrogen storage
  • Powertrain energy management and control
  • Distributed driving systems
  • Eco-driving
  • Eco-routing
  • Charge planning
  • Energy infrastructure for electrical vehicles
  • Vehicle and grid coordination
  • Connected and automated technology with application on NEV
  • Carbon neutrality and energy economics/policy

Dr. Chao Sun
Dr. Dong Zhang
Dr. Xingyu Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrified vehicles
  • powertrain and components
  • connected and automated technology
  • energy-saving technology
  • environment friendly
  • carbon reduction
  • infrastructure for electrical vehicles

Published Papers (7 papers)

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Research

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19 pages, 2173 KiB  
Article
Daytime and Overnight Joint Charging Scheduling for Battery Electric Buses Considering Time-Varying Charging Power
by Feifeng Zheng, Zhixin Wang, Zhaojie Wang and Ming Liu
Sustainability 2023, 15(13), 10728; https://doi.org/10.3390/su151310728 - 07 Jul 2023
Viewed by 1031
Abstract
This work investigates the joint daytime and overnight charging scheduling problem associated with battery electric buses (BEBs) at a single charging station. The objective is to minimize the total charging costs of all BEBs. Two important factors, i.e., peak–valley price and time-varying charging [...] Read more.
This work investigates the joint daytime and overnight charging scheduling problem associated with battery electric buses (BEBs) at a single charging station. The objective is to minimize the total charging costs of all BEBs. Two important factors, i.e., peak–valley price and time-varying charging power, are considered to depict real-world charging situations. We establish a mixed-integer programming model for the considered problem, and then conduct a case study together with sensitivity analysis. Numerical results show that compared with the existing first come, first serve rule-based charging solution, the charging schedule obtained by solving the established model via the CPLEX solver can save 7–8% of BEB charging costs. Hence, our model could be applied to improve the BEB charging schedule in practice. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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17 pages, 3949 KiB  
Article
Optimal Model of Electric Bus Scheduling Based on Energy Consumption and Battery Loss
by Yan Xing, Quanbo Fu, Yachao Li, Hanshuo Chu and Enyi Niu
Sustainability 2023, 15(12), 9640; https://doi.org/10.3390/su15129640 - 15 Jun 2023
Cited by 3 | Viewed by 1230
Abstract
The characteristics of electric buses make it difficult to estimate the energy consumption and mean that they are prone to battery loss; as such, fuel bus scheduling methods are no longer fully applicable. In current studies, the influence of these factors is ignored. [...] Read more.
The characteristics of electric buses make it difficult to estimate the energy consumption and mean that they are prone to battery loss; as such, fuel bus scheduling methods are no longer fully applicable. In current studies, the influence of these factors is ignored. This paper proposes an electric bus scheduling optimization model based on energy consumption and battery loss. Firstly, the LSTM (long short-term memory) is used to estimate trip energy consumption. Subsequently, these results are combined with the optimization objectives of minimizing the fleet size and battery loss amount. Limitations on the buses’ number, travel time, battery safety thresholds, remaining charge, and total charge are also considered. By controlling the different battery charge and discharge thresholds to minimize battery losses, the goal of sustainability is achieved. NSGA-II (non-dominated sorting genetic algorithm-II) is used to solve the model. The corresponding scheduling and charging scheme are determined. Electric bus route A is taken to validate the predictions. The results show that the annual fleet battery loss value decreases as the fleet size increases. The company has the lowest annual operating cost when the battery charge and discharge thresholds are set to [25%, 85%]. Optimizing the scheduling and charging scheme for electric bus can effectively reduce the operating cost. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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18 pages, 6660 KiB  
Article
Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data
by Zhifu Wang, Wei Luo, Song Xu, Yuan Yan, Limin Huang, Jingkai Wang, Wenmei Hao and Zhongyi Yang
Sustainability 2023, 15(2), 1120; https://doi.org/10.3390/su15021120 - 06 Jan 2023
Cited by 10 | Viewed by 2415
Abstract
Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in [...] Read more.
Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis, and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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14 pages, 3728 KiB  
Article
Decoupling Characteristics and Torque Analytical Model of Sharing-Suspension-Windings Bearingless Switched Reluctance Motor Considering Flux-Linkage Saturation
by Wenmei Hao, Jie Hao, Zhifu Wang and Yi Hao
Sustainability 2022, 14(24), 16633; https://doi.org/10.3390/su142416633 - 12 Dec 2022
Cited by 1 | Viewed by 1107
Abstract
As its name indicates, the bearingless switched reluctance motor does not have windings or permanent magnets on the rotor. This has the advantages of simple structure, high reliability and easy control. The sharing-suspension-windings bearingless switched reluctance motor inherits the above characteristics, and has [...] Read more.
As its name indicates, the bearingless switched reluctance motor does not have windings or permanent magnets on the rotor. This has the advantages of simple structure, high reliability and easy control. The sharing-suspension-windings bearingless switched reluctance motor inherits the above characteristics, and has obvious advantages in the research field of bearingless motors with its motor structure of decoupling torque and radial force. In this paper, the sharing-suspension-windings bearingless switched reluctance motor is taken as the research object. The finite element model of the sharing-suspension-windings bearingless switched reluctance prototype is established. The electromagnetic characteristics of the prototype are analyzed. As the premise of motor suspension, the structural decoupling of torque and radial force is analyzed and experimentally verified. Then, the flux-linkage saturation of the motor is derived at the position where the stator and rotor are completely aligned and the stator and rotor are completely unaligned. The torque model of the motor is derived based on the flux-linkage saturation, and the accuracy of the model is verified by the fitting comparison between the theory and the finite element simulation. It lays a theoretical foundation for the subsequent structure optimization design research of the sharing-suspension-windings bearingless switched reluctance motor. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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17 pages, 5137 KiB  
Article
Trajectory Following Control of Modern Configurable Multi-Articulated Urban Bus Based on Model Predictive Control
by Lu Shen and Liwei Zhang
Sustainability 2022, 14(24), 16619; https://doi.org/10.3390/su142416619 - 12 Dec 2022
Viewed by 1002
Abstract
The configurable and multi-articulated urban bus is a new type of urban vehicle with the advantages of road vehicles and urban rail trains. However, its articulated and long body structure will bring about difficulties in steering control and trajectory following. Moreover, the following [...] Read more.
The configurable and multi-articulated urban bus is a new type of urban vehicle with the advantages of road vehicles and urban rail trains. However, its articulated and long body structure will bring about difficulties in steering control and trajectory following. Moreover, the following carriages easily deviate from their expected path, leading to the fishtailing and folding of the compartment. In this paper, we propose a generic framework that allows the rapid building of kinematic models for the new train. By introducing the MPC theory, we design a trajectory tracking controller for a multi-articulated vehicle with an arbitrary number of carriages. To verify our models, we establish kinematic models and a trajectory tracking controller for a multi-articulated train with different number of compositions in MATLAB. Under the double-lane-change track and serpentine road conditions, the trajectory tracking of the train is simulated. The influence of the number of carriages, velocity, and length of carriage on the trajectory tracking are further analyzed. The experimental results show the feasibility of our method. Our findings thus provide significant guidance for the design, actual configuration, and trajectory tracking control of the new multi-articulated urban bus. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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22 pages, 10999 KiB  
Article
Design Method for Hybrid Electric Vehicle Powertrain Configuration with a Single Motor
by Bo Huang, Minghui Hu, Li Zeng, Guangshun Fu and Qinglong Jia
Sustainability 2022, 14(13), 8225; https://doi.org/10.3390/su14138225 - 05 Jul 2022
Cited by 3 | Viewed by 1886
Abstract
Existing design methods for hybrid power system configurations obtain new solutions based on experience, structure improvement or optimization, exhaustive searching, and the screening of schemes at the expense of less innovation and less efficiency. Furthermore, these methods lack mechanisms involving automotive theory to [...] Read more.
Existing design methods for hybrid power system configurations obtain new solutions based on experience, structure improvement or optimization, exhaustive searching, and the screening of schemes at the expense of less innovation and less efficiency. Furthermore, these methods lack mechanisms involving automotive theory to guide powertrain configuration design. In this study, a design method of configuration with a single motor based on basic schemes of speed and torque decoupling was proposed from the perspective of the hybrid electric vehicle fuel-saving mechanism. First, the coupling characteristics of speed and torque in the basic scheme were analyzed from four perspectives. Thereafter, new configurations that meet operation requirements were derived via configuration reconstruction, which combined the better basic schemes with brakes, clutches, and transmissions. A multidimensional evaluation and screening method based on dynamic performance, economic performance, and adaptability was built. A comparison of S-4 with Toyota Hybrid System, which was performed to demonstrate the feasibility of the design method, revealed that both configurations perform similarly in terms of economic performance, but the dynamic performance of the S-4 is greater by approximately 50%. The times required to attain 100 km/h from 0 km/h for THS and S-4 are 13.5 s and 6.69 s, respectively. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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Review

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18 pages, 1596 KiB  
Review
A Review of Life Prediction Methods for PEMFCs in Electric Vehicles
by Aihua Tang, Yuanhang Yang, Quanqing Yu, Zhigang Zhang and Lin Yang
Sustainability 2022, 14(16), 9842; https://doi.org/10.3390/su14169842 - 09 Aug 2022
Cited by 10 | Viewed by 2608
Abstract
The proton-exchange membrane fuel cell (PEMFC) has the advantage of high energy conversion efficiency, environmental friendliness, and zero carbon emissions. Therefore, as an attractive alternative energy, it is widely used in vehicles. Due to its high nonlinearity, strong time variation, and complex failure [...] Read more.
The proton-exchange membrane fuel cell (PEMFC) has the advantage of high energy conversion efficiency, environmental friendliness, and zero carbon emissions. Therefore, as an attractive alternative energy, it is widely used in vehicles. Due to its high nonlinearity, strong time variation, and complex failure mechanisms, it is extremely difficult to predict PEMFC life in electric vehicles. The uncertainty of life predictions for the PEMFC limits its wide application. Since it is particularly important to accurately carry out PEMFC life predictions, significant research efforts are directed toward tackling this issue by adopting effective methods. In this paper, a number of PEMFC life prediction methods for electric vehicles are reviewed and summarized. The goal of this review is to render feasible and potential solutions for dealing with PEMFC life issues considering dynamic vehicle conditions. Based on this review, the reader can also easily understand the research status of PEMFC life prediction methods and this review lays a theoretical foundation for future research. Full article
(This article belongs to the Special Issue Emerging Research in Intelligent New Energy Vehicles)
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